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We can tell that the inferencing costs for many of these models are low enough that these models are being sold close to real costs on the basis that many of them are open weight and available from third party providers who have no incentive to subsidize them.

I think the frontier labs will need to drop their high per-token prices at least for their low and mid-level models for the reason that several Chinese models (at least Qwen, DeepSeek, Kimi and GLM) are "close enough" that with the right harness they are cost effective alternatives.

They won't necessarily need to close the gap - at least not yet -, because these models won't necessarily compete at the same token counts. E.g. at least some of them need to do far more work to solve the same problems.

But, yeah, the prices will come down one way or the other.

At the same time, even the subscriptions for the cheap Chinese models are probably subsidised, and those subscriptions are likely to get less generous over time.

One aspect Paul Kedrosky mentioned recently is the concept of „duration mismatch“. The price per token goes down over time (either because the AI vendor reduces due to competition pressure, or because customers are now incentivized to use older cheaper models). But datacenters are financed through debt, with the assumption their revenue increases over time. Quoting him: „[AI vendors are] paying for a fixed cost with a depreciating commodity“[0].

So you have on one end the token revenue trending down, on the other end the training cost going up for the next frontier models, and you need to pay back your 10y debt.

0: https://youtu.be/wGZboZcSGDY?is=64GuKyqBh_4aSjTE

Don't worry, they'll just lobby to ban Chinese models instead to keep their token revenues high.

> Compounding the problem, labs in China often release dual-use capable models as open-weight. Once a model is open-weight, safeguards that do exist can be removed, making the model available to any state or non-state actor to use for malicious purposes, including the cyber and CBRN misuse those safeguards were built to prevent.

https://www.anthropic.com/research/2028-ai-leadership

Raise them, more likely. NVidia says that GPU hardware prices won't decrease until at least 2030. The world is out of fab capacity.

Most sane US companies will disallow use of cloud-based Chinese AI providers, because everything including code, data, PII, etc is being sent to them.

> Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China?

Raise, they are going to raise the prices. We will spend more on AI infrastructure in 2026 and 2027 than the gross sales of the entire global software and services sector. Current pricing is at a major loss for current providers.

Why would I even pay for deepseek? I get deepseek v4 flash for free with opencode. If I somehow run out of tokens for the day, I can just then on my vpn

Per token costs will fall, but the harnesses will get more token hungry. Instead of just centering the div it’ll spin up a battery of agents to architect, critique, advise, code, review, refactor and so on.

If Anthropic are then they are making a big mistake, their token hungry Claude code is far too greedy

They're going to need to bring in a few trillion dollars fast to meet wall street expectations. Expect prices to rise.

> Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China?

Are they even making money off them now ?

> Do we know that AI providers are going to keep these per-token prices, or eventually lower them because of competition from China?

I genuinely do not know how prices can get lower from the current major providers in NA without the whole market collapsing. Everyone is spending copious amounts of money to presumably make more money back.

id be amazed any american business will aend data to china

API prices of Anthropic, OpenAI, and Google are massively inflated.

https://martinalderson.com/posts/no-it-doesnt-cost-anthropic...

There's no way that all AI inference providers are colluding and/or all running at a massive loss, meaning the cheap Chinese model prices must be the real cost it takes to run frontier-class models PLUS their margin.

Look at Deepseek 4 Pro. https://openrouter.ai/deepseek/deepseek-v4-pro/providers Deepseek and Baidu are subsidising prices but they probably train on inputs. I have no model training and ZDR in OpenRouter enabled, and the first provider that shows up there is Deepinfra, significantly more expensive than Deepseek. BUT much cheaper than Sonnet 4.6 and ChatGPT GPT-5.4.

It's pretty simple; organizations are willing to tolerate paying $1500/month/engineer, which seems to be roughly inline with "normal" consumption for most full-time engineers. If that number grows significantly, then I bet companies will start exploring flash models more, as you propose.

The easy decision is to just go with the biggest SOTA model you can afford.

But this overlooks the other critical part of getting the most out of these things: the harness. I run an autonomous plan/design/code/build/test pipeline with agents using my own orchestrator. Different models are better at different stages, and I use LLMs to judge the output between them. Not everything needs Opus 4.8.

The harness provides both the scaffolding to get the right things into the model, and the right things out. But it also lets you dictate which model does which work.

It's the pipeline, not the model, that gets you quality at a given token budget.

There is something about using the most advanced tooling possible. Why would you pay for IntelliJ, if Eclipse can do the same thing a bit worse?

You want to master your craft, develop "optimal" systems, understand where things are going by utilizing SOTA.

You can call it FOMO, but you get the point.

Is your argument that $1500 / mo is too much? Why would the engineering team not be more rigorous in their model selection given a constraint?

I wonder to what extent models should figure out which model to forward a query to. Or perhaps the big models could learn the difference between an easy and a hard question and charge accordingly? Perhaps, if it can measure complexity, even generate a quote?

Small models are fine for small coding tasks but I don't see why big ones can't be broken down most of the time.

This a thousand times. The bigger models also have a habit of overcomplicating things.

> Don't ask LLMs for big changes

> Review everything and point them in the right direction

Sorry upper management doesn't care. That's an engineering problem that you need to solve.

I'm legit annoyed at opus 4.8 at any setting above 4.8.

I believe it can be great for vibe coding, but mundane day work? Hell no, I'd rather work with Haiku. It's too slow, checks too many things, it's annoying as hell.

My usual rule of thumb for the US is north of double the received compensation but something in that range sounds reasonable with such high compensation. It's actually really interesting and underappreciated how that fully-loaded cost varies from country to country. Canada (for most salary ranges) is about half again instead of double owing to the insurance portion coming out of income tax rather than being a hidden expense so Vancouver ends up being attractive for trading 160k USD for like 120k CAD in compensation and then also lowering overhead from 100k USD down to like 60k CAD. The savings can be extremely dramatic.

> $330k/year

For a traditional software engineer? I retired last year after 3 decades and my salary was about the same as it was in the early 2000's at the last company I was at. Maybe I should have negotiated more but I thought only FAANG paid traditional pre-AI engineers more than $250K.

I’ve even heard the rule “twice the salary” being used here in EU, but the tax and insurance burden may be higher. All kinds of those are based primarily on total payroll amount.

It is also possible that capping at $1500 will give you ~99% of the benefits. So even with gains that are much higher, a cap could be a rational decision. Also, most decisions, especially around AI aren't exactly rational, so I wouldn't read to much into this number.

Both metrics are valuable.

If one uses AI minimally and is able to out perform peers who are maxing out AI spend, one might want to use that in salary negotiations.

"$330k/year" Lol. I thought I clicked on hacker news 2022.

It’s also worth noting that’s the peak benefit. Expect most engineers to not hit those limits on the regular (if at all, since limiting this puts skills in focus again), and that limit to come down over time as the easy processes are automated and humans are re-tasked with harder problems relative to their TC.

This is not a good bellwether for the AI industry, including its adherents. Their growth assumed a level of indispensability that’s not being reflected in hard numbers and real costs, which lends credence to the notion that these IPOs being fast-tracked are meant to try and cash out before the bubble really pops in earnest. There’s no way consuming enterprises are going to pay such insane costs for such minimal uplift in the long run, and the AI companies can’t keep offering subsidized tokens via subscription plans at their current pricing.

Because companies are betting that this spending will allow them to reduce cost by firing people.

Right now the AI LLM PRs we're seeing are just introducing more work for other people, while these so-called builders are looking good with their new dashboards and functionality they're demoing.

But you can't talk to them about the flow of the code. You can't ask them for their thinking as to why certain things are.

It's not built up from the ground with experience from x people taken into account. It's materialized from nothing, with no foundational separation, and barely any abstractions.

No one wants to touch it. The PRs are too large, and the 'authors' of the PRs aren't on call with us.

They get all the glory, but do none of the work.

It's kinda like designing a house and then sending it to an architect and engineer saying: make this work.

That's just a non sequitur. "companies are already paying thousands per seat" has zero correlation with something being a fad or not. There are much more reasonable rationales explaining why companies are acting the way they are than "because AI coding is not a fad"

I would use these exact facts as a sign that it's maybe not what it seems. It's much too big and too fast to feel stable. It might keep at that level, increase even more, or drop down to a saner level of use / allocation.

Fear of loss to competitors embracing a technology creates a fear driven adoption.

Let me ask you this: is any technology worth so much break-neck adoption without first seeing clear evidence of ROI? No. The adoption is irrational.

“AI coding is a fad” is not just one big camp of similar-minded people. Different groups have to give up on their pre-existing beliefs in order to be ok with AI coding.

Think of people who were very strict with variable names. People who pushed for multiple-levels deep of abstractions for a single API logic that’s not going to be reused. People who believed that coding is craft, rather than just a process to get to the end during work hours. This makes most of these people’s points more-or-less moot.

I was in some of those camps, but I’ve seen coding evolve in the last 15 years. So I understand that these priors need to be updated, as most arguments don’t apply to today’s world.

Because the vibe coded stuff is sometimes great, sometimes it breaks stuff, sometimes it breaks things that we fixed multiple times earlier. The PRs are too large, nobody can review that mess and you better be on call for your deployment. Maybe it will get better, maybe not. I dont know yet.

There is a whole spectrum between "ai coding is a fad" and "unlimited tokens for every employees we don't even care if it actually ends up being a net positive financially"

As a side note, I wonder when we'll hear the first reports about employees reselling (parts of) their token budget.

Probably not worth it risking your job for a 200$/month good, but at 5K, I'm sure some folks will be tempted. Especially if companies do stupid things like token usage leaderboards.

perhaps the personal computer? Companies were spending 3-5k (10-15k inflation adjusted) on every employee for just hardware.

everyone making comparisons to the dotcom bubble seems misguided. this is clearly computing 2.0 imo

I still believe Scrum is a fad and yet companies have been spending obscene amounts on to push it down developers' throats for decades now.

> Which other tool went from nothing to this level of acceptance so quickly?

NFTs? My company had nothing to do with blockchain but I ended up working on NFT integration regardless.

>Why there are so many people that still believe that AI coding is a fad?

Because there's not a single piece of evidence that this has improved the quality of the delivered software, or for that matter even the speed of features any of these companies produce, in fact if anything the opposite.

The point of software development, the hint is in the name, is to develop software, not consume tokens. If Uber was now full of 10x engineers the stock price of Uber would be up, not down on a yearly basis. Hilariously enough the only company whose stock price is up appears to be Antrophic

Because writing huge amounts of code is easy for humans too. Agents already proved that they can do it. But are agents able to maintain it? I do not know and unless I know for sure, I am not fully committing to AI generated code.

i.e. I am able to write about 1k lines of code of "acceptable" quality per week. Which means in 1 year, there will be about 5Ok LoC. I am pretty sure, that I would have to spent like 60-80% of time to maintain 1st year code and the rest to make new features in the second year so I would have to hire more people and spent time to onboard them to maintain velocity. All of that are rough estimates, probably overoptimistic and way worse in 3rd year. Good luck doing such estimates with code agents. Even worse if you already have huge amounts of legacy code.

Why are there so many people who mistake simple anecdotes for actionable data? Why do the majority of businesses fail rather than succeed?

Because we have spent a lot of time and money using AI to generate code and have been unimpressed with the results.

As for why they got accepted so quickly 1) the industry's long running desperation to deskill computer programming 2) the addictive psychology baked into LLMs "That's an elegant solution! Shall I ... ?"

It's cope. People desperately want to believe that AI coding is going away so that they can go back to partying like it's 2020.

So there's a huge number of HN posters claiming that the price of tokens will go UP over time rather than down (that's how Moore's Law works, right???) or that code bases that AI contributes to will spontaneously combust, or something.

How is tok/s not a bottleneck I? I assume most people still use ai agents interactively rather than leaving them to do their own thing during the night.

I find anything below 50 tps or so entirely unusable...

Regardless its Apples to oranges anyway, inference is quite cheap for open weight models its just that Claude and OpenAI can charge very high margins compared to e.g. DeepSeek or various provider on OpenRouter since open models are a commodity.

I think companies will eventually just buy a local AI server.

Using local hardware is expensive when it's running a complicated software stack that can break in 10,000 different ways.

These eventual local AI servers will just talk some protocol for AI and sit in the corner and nobody will think about them.

I guess they still might need access to various systems, so idk. Eventually I think someone will offer "AI in a box" though, running the latest open model or whatever.

I agree on the basic point, but running $1500/mo's worth of SOTA local AI is non-trivial already, and that's a figure for a single seat. That's equivalent to generating at least 20 tok/s on a 24/7 basis, in fact probably quite a bit more than that (because open-weight models are vastly cheaper than proprietary ones even when served from reputable Western providers - reaching the same spend would take around 100 tok/s or more, which is well within datacenter hardware territory).

You could probably reach the former figure on a prosumer platform but only for very special workloads. If you spend a lot of time on prefill (which is common for agentic workloads) the outlook is even worse since that's a significant constraint for any on-prem AI.

You’re way better to run your own on premise models. Laptops are depreciating assets, do not benefit from economy of scale, have fixed specs, result in a fragmented fleet where you need to keep models up to date. Without talking about power consumption and cooling issues. I really don’t see why companies would go that direction

> it's WTF did Uber build with all of that spend?

You can ask the same for the median 330k salary in the US for Uber Engineering... and being a bit snarky, attending Uber engineers talks here and there at a few conferences, looks like. they love to (re)invent internal tooling/platforms. That's pretty expensive on its own.

EDIT: I'm not saying that Uber's engineers didn't add value to the company, they absolutely did and handling the scale up they had to handle is not an easy feat. But I do challenge the notion of "what features did they create with that (LLM) spending?" of GP.

128GB machines can't run anything locally that is even nearly as capable as a frontier model like Claude. We can get an idea from deepseek v4 pro being 1.6T model, requiring approx. 860GB VRAM to run.

at their scale they could also just run a large on-premise or rented (basically still cloud, but cheaper) GPU cluster and run through that. fixed costs, even license a SOTA model’s weights if you’d like

> How did it meaningfully impact their revenue in a positive direction?

It probably allowed them to avoid hiring as many people to build a certain amount of software. Even if it didn't increase revenue, it could have lowered human labor costs.

> 128 GB machines that can run local LLMs are a bargain even if priced $5-8k.

Don't forget the energy costs. Searching around, advanced models use an average of 25 Wh/1000Tok.

$1500/month gets you about 150M tokens.

At the aforementioned energy/token, that's 3750kWh.

What are your local office electricity rates/tariffs? (Hint: they are going up because of AI data centers). Even if my price and energy assumptions are wrong above, you probably aren't going to get the rates that the hyperscalers do.

Even at cheap (i.e Texas) retail electricity rates, that many tokens will probably cost you hundreds per month. In most other electricity markets, probably far more.

Right - the future of LLMs is like ol' windows XP+Dell. Commercialized "things" you run locally offline, co-designed with hardware, with a known productivity suite, and large businesses building the next generation thing and suite with 18mo release cycles (ish).

I don't think it's necessarily what Uber build, but the gained productivity. If the engineers use the AI tools the correct way, it can drastically increase the productivity and that means they can actually use the LLM as a junior or an associate engineer. $1500/mo is way cheaper for that level of productivity where as they would have had to pay far more for a human engineer.

Even if companies decided to move away from expensive models from the major labs, it probably much more economical to pay a cloud provider to host some open weights model which could then be amortized across all (internal) users and do inference at a substantial batch size, rather than giving everyone their own hardware -- which means the company would need to provision for peak usage and inference at batch size of one.

Your last question is really important. What did they accomplish with all that spend?

I suspect there’s some mass delusion with respect to actual accomplishments as a result of LLM use. Sure, things are moving faster, but does it matter?

If you believe a 128gb machine that is essentially DGX Spark in a laptop chassis can run models comparable to SOTA you either never ran open models on hard tasks, or you aren't scratching the surface of SOTA closed LLM capability in how you're using them.

I am wondering more and more if this becomes true as these smaller models take off. I might be old fashioned but I have yet to crack the workflows some of the hype people spout like Claude codes Boris where he and others talk about running hundreds of agents overnight.

I have still found the sweet spot for me is using LLMs but I am still in the drivers seat.

>WTF did Uber build with all of that spend? How did it meaningfully impact their revenue in a positive direction?

Uber (and quite a few bay area companies and startups) can afford to spend that money. There is no expectation of profit, Uber lost ~62B and growing: https://uberlosses.com/

$1.5kpm for SOTA. 128gb you run DSV4 Flash.

> WTF did Uber build with all of that spend?

WTF did anyone build with all that spend? Despite all the feel-good anecdotes about how productive folks feel using ai coding tools there's a deafening silence when it comes to actual, demonstrated efficacy. How can we be this far entrenched in these workflows and still not know whether they actually do anything useful?

I think probably the correct spend is something closer to 10x that if people can figure agent coordination problems out. It's not even really about capability at this point, it's about keeping track of what agents are doing.

You can't get an edge using local models, these guys may have competitors that will spend on SOTA models. They won't likely ever consider local machines even for some offloading scenarios, the complexity and costs will be even higher.

18k/yr? None of the LLMs generate anything like that in value!

Why even pay them at all? Just lock them in a cell and give them a bowl of rice.

> All fair but very much assumes that salaries are rational. Why do we pay some engineers 10x as much for the same role just because they are in a different location?

Who's this "we" you're talking about? Are you a software engineer or a temporarily embarrassed billionaire? Do you think the rational thing is to pay the lowest regional salary worldwide?

As well as rational vs irrational they are also just different types of spending.

Hiring someone vs paying a vendor for a service:

- different level of commitment

- might tie your org to a physical location

- different legal risks

- shows investors a different picture (probably this would even influence a bank loan)

- manager has to fight a different bureaucracy

Not to mention that comparing the cost of a hire by looking at their salary is pretty dumb. ISTR hearing at Google that the overall estimated cost of employing a SWE is like 4X their compensation? Can't remember the exact figures though.

There are a lot of places in Europe where 1.5k$ is more than 50% of the total cost of an employee.

And the obvious question: what it's the cost of that revenue? Because it looks huge but ...

Are you saying there are only 30 million people employed in white collar jobs in the world?

45 billion / 1500 $ is 30 million workers. How did we arrive at 30 million?

World bank says there are 3.7B employed humans. Putting the total addressable market at around 67T if all of us spend USD 1.5k on tokens every month. This lines up well with current forecasts from the major AI labs

That's a bold assumption. Increasing costs by roughly $18 000 per employee worldwide is highly unlikely. For reference even at FAANG in Europe, that would be a 7-15% cost increase for a senior developer. More like 15-30% for non FAANG and even more for non-European markets.

Both the Anthropic and OpenAI "Enterprise" plans include per-developer analytics:

Anthropic: https://support.claude.com/en/articles/12883420-view-usage-a...

OpenAI: https://help.openai.com/en/articles/10875114-workspace-analy...

Second here. From recent Alibaba Qwen conference: the all-in-one box (DC in a box - I think I was called Apsara, 0.6x0.6x1.5m) plug and play, 1.5TB GPU RAM, capability to run in a fully air gapped environment, any open models... All of that is roughly $300k one time. And this box can do non LLM tasks as well. Performance (throughput) around 20k t/s. Delivery time - around 2 months. For any medium sized company its perhaps cheaper to just buy it once than spending 1.5k for cloud per user

Decent vs best-money-can-buy. Further, a self-hosted LLM will be much slower.

I'd think for most companies the pace of change is too high at the moment. Give it a few years, a bit of a plateau in the improvements in frontier models and I can't see how many of these companies don't implode under the weight of competition on inference prices.

I also randomly wrote some code in a bind yesterday, while I was on the toilet, and it felt so strange. That was the first I'd written in probably 6 months.

Isn't all the "knowledge" just text files? I've transitioned between services easily by simply copying the text files.

This.^ I realized this first when moving a design spec from Claude chat to Claude Code and panicked. I literally had to build something like Notion but for agents to act as a portable memory between all cloud and local models and agents. But honestly it paid off!

If you are interested you can try it out at markbase.cloud (disclaimer and all that). I am not charging for it.

What knowledge?

Unless you work in some obscure domain, chances are that any general "knowledge" Claude has "learned" is already public data somewhere.

If you don't believe me, launch Codex and immediately start working on the same project (s). You might discover that all the knowledge accumulated means almost nothing.

Not worried at all. Switching is trivial. Rebuilding context isn't very difficult and harnesses are a dime-a-dozen.

My favorite solution to this is to use the Cline coding agent, which is open and allows you to easily switch between different providers and models.

Knowledge in there?

Where is the knowledge stored?

All of my knowledge typically gets stored in plans outside of the agent?

And each agent window gets archived regularly, anyways.

If the premium models are just about 10% better - that could justify the price vs. self hosting a ~0.5-1T open weights model.

Remember that utilization of these huge racks will not be 24h/7, and these are usually not GPU intensive shops that would train models on the spare compute. With prices of 100-200k USD and north with ~2 years lifetime, that would be hard to justify financially.

Self hosting could easily amount to ~1000 USD a month amortized across many developers. In rush hours - there will be hard rate limits.

Would that 1500-1000=500$ monthly USD justify the 10% decrease in "AI Productivity" ? I guess not. In most cases.

For everyone that asks me around, I'd say that in short term, unless there's a really good reason to self host these coding assistant models, then the big 2/3 coding assistants providers are the better choice.

No one got fired from licensing claude code.

I just went through a similar discussion in my $WORK (traditional finance company on NYSE with average IT expertise) and I think the thought process is as such: it's one thing to just give your stellar dev/hacker a beefy GPU server and run whatever model they can run; it's another thing to maintain such platform for company wide. You would need human resource (likely way above normal software dev paygrade) to understand and maintain such models, maintain backend, availability etc. All these extra hassle make it just easier to pay a top tier external lab + slap a reasonable spending limit on everybody.

Why do you think it would be more common? The pooling of GPUs to serve multiple users and connecting to docs/datalakes while respecting security controls, as a start, is non-trivial. You'd end up paying a team to manage that.

There’s probably plenty of money to be made in LLMs as a service - but not enough time has passed for the commodification to occur. I’m with you in that when the dust settles I don’t think any of the frontier model providers will have a moat. Just like during the dotcom boom a catchy URL and a webpage that could accept payments wasn’t a moat, either.

Where are you buying the GPUs to have enough compute to run a medium size buisness?

> I've tried the open weight models ...

You tried that on a personal machine for yourself once. It's completely different calculation when serving a model to 3000 employees with ever evolving hardware and software requirements. You'll need dedicated hardware in data centers and experts to run them. A company will need to figure out how to manage acquisition, assets and expenses plus 1000 other things, in addition to its actual business. Guess who has figured out all of that already? AWS/Azure/OpenAI etc.

For the same reasons companies are not building data centers for their "regular" hosting and storage needs but put things on AWS, Azure etc.

It costs money to maintain the hardware and hire experts to manage the services. For something as common as LLM models, there is absolutely no reason a company serves models on their own hardware unless they are maniac about sending bytes to AWS.

> didn't even come to $600.

That's still in the ballpark. A modest change in your usage habits or workload could easily get you there.

is this with a subscription or pure API billing?

That's just Simon Willison since LLMs came out. It's glaringly obvious that he's a paid shill.

I'm in a similar boat - it's hard to measure, but let's say you pay an engineer 150K. Giving them a tool that costs 15K a year is effectively a 10% increase in that expense.

If we were seeing 3X, 5X etc improvement from individual engineers, that 10% increase in expense would be a fantastic investment (even 3 engineers for the price of 1.1??!). I have a feeling they are just not seeing that much of an improvement.

I would assume that at least one of two things are true:

1. They're costs are so so out of control that they need to impose a blanket cap immediately. Figuring out an allocation mechanism that can be deployed company wide is time consuming and they need to staunch the bleeding immediately, despite it being obviously suboptimal.

2. The few people who should have unlimited tokens were given exactly that. No reason to introduce such nuance to a public PR move. The hard-cap limit is a great negotiating posture with token providers.

You must not be using coding agents. You can sneeze and spend $1 on Opus in Claude Code.

probably will get fired for lack of performance.

no because it does not come from the same budget

There is no evidence that per-token inference prices (which is what Uber is setting a cap on) is subsidized.

afaik, enterprise plans are not subsidized. its 20$/seat+api pricing. Unless you are saying api pricing itself is subsidized.

True but they will raise prices slowly so people will optimize their workflow so they aren't just throwing as much inference as fast as possible like the current state. Right now you should do everything you wanted to try out because it is cheap (as long as you don't become dependent ... the risk).

I understand current Codex $20 sub is worth about $480 GPT5 api credits.

The inference prices for very large open models would indicate that Antrophic's and OpenAI's margins are quite large.

It's not. They recently forced enterprise customers onto API billing instead of the cheap consumer pricing. Now the pricing is brutal.

$1500/mth is token pricing.

Your other plans are fixed price with rate limits where you get more tokens than the dollar equivalent you pay monthly. These plans are economical only if majority of users spend less tokens in $ than the plan's costs. This subsidizes the gap vs. power users who spend multiple k$ monthly in API tokens.

I'm on a $100 Claude Max plan, my usage is only about 50% of the plan limits, but in the last 30 days my usage was equivalent to API token spend of $1850. If you save all your Claude Code conversations, the saved files include API costs and you can calculate this yourself.

One of my most expensive sessions cost me over $100 in token spend in a single evening. I'd just found out that the time tracking & invoicing SaaS I use is increasing their monthly pricing by 2.4x - so I assigned Claude Opus 4.8 to recreate the entire SaaS for myself, and load in 13 years of my historical data. I've only completed a full read-only implementation so far, with adding & editing of records still to come, but I do expect Claude will have fully recreated the entire SaaS for me at an API cost less than a single 1 year seat of continued subscription to their service. And since I'm actually on a Max plan, it didn't actually cost me $200 of tokens at all.

coff i would not buy the Bending Spoons IPO coff saaspocalypse

I could ramble on about where the other $1750 of usage goes, but I imagine it's similar for most heavy Claude / AI users. Interactive coding sessions, a daily personalized podcast, some automated overnight agentic "proactive" sessions, a daemon that wakes up if I send Claude an email or voicetext to check something when I'm out. I've also noticed that if Claude's tool-use goes haywire & Claude gets confused or lost, sometimes a single email reply session that would normally be just $1 of API might spiral to $12 of API while it bangs its head against trying to run a program that's in a different folder to the one it's currently in. Sometimes a simple 'pwd' would save you a lot of headache, Claude....

Uber is likely on an enterprise plan - these charge tokens at API cost, which can be much more expensive than the $20 flat rate.

No, that's not what it means at all even if just doing it purely in math terms. Really it is just a reasonable amount to cap at to stop the long tail of super spenders (tokenmaxxers). You could also call it "the amount of AI spend after which Uber has decided there is diminishing returns for the average engineer".

It's not so simple to determine and generalize how much value AI adds. It's going to be different on a per-company basis and a per-engineer basis. It's also affected by the competitive market place and how many other companies are using AI for their engineers.

For example, what if you're a tiny startup and you're considering whether to hire an extra engineer or do all the coding yourself. I would estimate that AI is worth far more than $18,000 a year in that situation where you might reasonably decide to put off hiring an engineer.

I find it really doubtful anyone has managed to quantify that in any meaningful way. Seems like mostly an arbitrary number. Also the article does claim that's its actual several times more than 18k if you are fine with using Codex, Cursor or etc. when you Claude tokens run out.

Their initial budget for determining how much value AI adds is $18,000 per engineer.

Not really. There are clearly diminishing marginal returns, so it's likely that the first $2,400/engineer/year adds >>$2,400 of value, even if 18,001st $/engineer/year adds <$1 of value.

It means Uber thinks they can sustain that level of expense. Whether engineers at Uber are representative of the rest of the work force is an easily debatable question.

It's among a wave of fresh "non-insane" takes on AI in the enterprise. Maybe we can reel things in to a sustainable level before a giant bubble bursts.

All new/renewing enterprise contracts with Claude Enterprise and ChatGPT Enterprise no longer offer usage-based subscriptions, but instead will charge API pricing for all tokens consumed, and as you've said, the subs are better deals than raw API pricing.

BigCo's are not using the plans we are using, they can't.

Do you believe the same people were saying those things? (Were they really?) The idea that "different attitudes towards labor have been expressed by different people" doesn't feel too remarkable

unless they changed something in the like 2 months (edit: besides implementing a cap for claude code specifically, since other tools already had caps) since ive left my job there im pretty sure 1500$ is the very max you can use after maxing out free calls, initial budget, then 2 extensions individually reviewed by your manager

higher ups pushed for these last 2 years to be AI focused so I don't think this restriction is a measure of "don't use too much AI" as much as it is a measure of "don't use only 'manual' AI tooling" since we had a dozen more specialized tools in-house running locally or otherwise that didn't count towards the budget

But is it an accurate number? Does AI reach diminishing returns after $1,500/month, or is that all they are willing to risk/burn to stay in this game?

I don't think loading up a single context window costs $1,500. Which limit are you talking about?

Is this inside knowledge, or speculation?

Or be smarter about their usage. $50 on tokens per day can get you a long way.

I don't think at $1,500 you're not forced to code on your own at all, in the sense of typing code. You're simply forced to not yolo-max twelve parallel agents at all times.

It’s not just about the model but also setting up the system to create and share compute (GPUs) which is quite complicated on its own. Ubers primary business focus isn’t infrastructure.

Electricity actually is only a small part of the data center costs. There are challenges in getting enough electricity that create problems, but the cost of the electricity really isn’t an issue.

Technically, tokens travel both ways.

I think there's too much variance between what model you're using and how much you turn your brain off. If I just paste a ticket number into 4.8xHigh its going to use a lot more tokens than if I read the ticket, tell Sonnet what it needs to do, make my commit, run unit tests myself, etc.

Outside of coding what other tools expend that kind of tokens? People are not creating that many slide decks or videos are they?

The subscriptions are not available to enterprise users. Enterprise users must pay per-token. A $200 subscription gives you roughly the equivalent of $1500 in per-token billing.

It costs a lot more than $18,000 to hire a decent developer, pretty much anywhere in the world. Also using a model is better than another developer in some ways, because there aren't two independent minds trying to work with each other.

I would not be surprised if they have engineers vibecoding 2-3 projects each simultaneously, nonstop, on largely un-moderated review-suggest-iterate-test feedback loops.

All the code gets summarized and fed into their manager's agent contexts, probably duplicated several times across levels and departments, with some generated back-and-forth emails pinging around the org chart, eventually generating 2-3 long-winded reports that nobody will read chock full of generated visualizations that can all get consolidated into a generated slide deck that they'll show (maybe, at some point) to a handful of humans with more money than a human brain can conceptualize to demonstrate all of the innovation they're doing.

I am increasingly convinced that many of these companies are dead trees whose only function is to burn money lest it fall into the hands of the peasantry.

You are paying account pricing. Uber is paying API pricing.

You're $100/m plan is likely equivalent to thousands of dollars of API pricing. You are being subsidized by the companies using AI.

just don't care about the output. Produce more. Don't check the results.

Typescript is also hugely represented. My projects are TS in a big way, where I have no experience with it at all.

if you have more than x seats, you have to use Enterprise pricing as far as I know which is pay as you go with a pool.

Poor people! Thinking takes calories

Even more things can be done without any models just as well.